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Machine Learning Project

( Gender and Emotion Recognition )

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We have Followed the Following Steps:

Step (1) Import Libraries

Step (2) Load 5 Datasets

          Step 2.1: Load Dataset-1

          Step 2.2: Load Dataset-2

          Step 2.3: Load Dataset-3

          Step 2.4: Load Dataset-4

          Step 2.5: Load Dataset-5

Step (3) Extract MFCC Features

          Step 3.1: Extract MFCC Features from Dataset-1

                      Step 3.1.1: Print MFCC Features-5

          Step 3.2: Extract MFCC Features from Dataset-2

                      Step 3.2.1: Print MFCC Features-5

          Step 3.3: Extract MFCC Features from Dataset-3

                      Step 3.3.1: Print MFCC Features-5

          Step 3.4: Extract MFCC Features from Dataset-4

                      Step 3.4.1: Print MFCC Features-5

          Step 3.5: Extract MFCC Features from Dataset-5

                      Step 3.5.1: Print MFCC Features-5

Step (4) Pre-Processing

          Step 4.1: Combine Feature of all Datasets

          Step 4.2: Check Data Distribution

          Step 4.3: Data Sampling

          Step 4.4: Data Scaling

          Step 4.5: Label Encoding Outputs

Step (5) Split Dataset in Traing and Testing

Step (6) Train CNN Classifier

          Step 6.1: For Gender Classification

                      Step 6.1.1: Training

                      Step 6.1.2: Learning Curve

                      Step 6.1.3: Save Model

          Step 6.2: For Emotion Classification

                      Step 6.2.1: Training

                      Step 6.2.2: Learning Curve

                      Step 6.2.3: Save Model

Step (7) Train MLP Classifier

          Step 7.1: For Gender Classification

                      Step 7.1.1: Training

                      Step 7.1.2: Evaluation

                      Step 7.1.3: Save Model

          Step 7.2: For Emotion Classification

                      Step 7.2.1: Training

                      Step 7.2.2: Evaluation

                      Step 7.2.3: Save Model

Step (8) Comparision of CNN and MLP

Screenshot 2021-12-13 084300.png

Step 1: Import Libraries

Step 2: Load 5 Datasets

We have used the following 5 Dataset
(1) SAVEE Database: Datset Link

(2) EmoDB: Datset Link

(3) CREMA-D: Datset Link

(4) TESS: Datset Link

(5) RAVDESS: Datset Link

Step 2.1: Load Dataset-1

Step 2.2: Load Dataset-2

Step 2.3: Load Dataset-3

Step 2.4: Load Dataset-4

Step 2.5: Load Dataset-5

Step 3: Extract MFCC Features

Step 3.1: Extract MFCC Features from Dataset-1

Step 3.1.1: Print MFCC Features-1

Step 3.2: Extract MFCC Features from Dataset-2

Step 3.2.1: Print MFCC Features-2

Step 3.3: Extract MFCC Features from Dataset-3

Step 3.3.1: Print MFCC Features-3

Step 3.4: Extract MFCC Features from Dataset-4

Step 3.4.1: Print MFCC Features-4

Step 3.5: Extract MFCC Features from Dataset-5

Step 3.5.1: Print MFCC Features-5

Step 4: Pre-Processing

Step 4.1: Combine Feature of all Datasets

Step 4.2: Check Data Distribution

Step 4.3: Data Sampling

Step 4.4: Data Scaling

Step 4.5: Label Encoding Outputs

Step 5: Split Dataset in Traing and Testing

Step 6: Train CNN Classifier

Step 6.1: For Gender Classification

Step 6.1.1: Training

Step 6.1.2: Learning Curve

Step 6.1.3: Save model

Step 6.2: For Emotion Classification

Step 6.2.1: Training

Step 6.2.2: Learning Curve

Step 6.2.3: Save Model

Step 7: Train MLP Classifier

Step 7.1: For Gender Classification

Step 7.1.1: Training

Step 7.1.2: Evaluation

Step 7.1.3: Save Model

Step 7.2: For Emotion Classification

Step 7.2.1: Training

Step 7.2.2: Evalution

Step 7.2.3: Save Model

Step 8: Comparision of CNN and MLP




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Thank you so much

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